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Diagnostic Constructs of Major Depression in DSM-5: Current Critiques and Future Directions.

Researchers

Seon-Cheol Park, Kiwon Kim, Yong-Ku Kim

Abstract

The diagnostic frameworks for major depression in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and DSM-5 revision process (DSM-5-TR) have been largely defined by the significant transition from a categorical approach to a hybrid concept incorporating dimensional concepts. The main updates in the DSM-5 diagnostic framework of major depression are the addition of hopelessness as a descriptor for depressed mood, the removal of the bereavement exclusion, and the introduction of specifiers, including "with anxious distress," "with mixed features," and "peripartum onset." The DSM-5-TR has newly added the depression-related diagnostic entities, including unspecified mood disorder, persistent grief disorder, suicidal behavior, nonsuicidal self-injury, and hikikomori. However, the symptom-based operation criteria for major depression remain challenging, including high clinical heterogeneity, high comorbidity, transdiagnostic nature of overlapping symptoms, and lack of integration with neurobiological markers. To overcome these limitations, the etiology-based classifications with integrating biomarkers and the digital phenotyping with ecological momentary assessments have been suggested as diagnostic practices in the level of research. The neuroinflammatory biomarkers and brain imaging have been regarded as a promising tool, but their issues regarding reproducibility and cost remain challenging in clinical applications. In addition, the digital tools with big data, wearable devices, and natural language processing may enhance the diagnostic precision, despite their issues of ethics and standardizations. Future diagnostic frameworks can be discussed in terms of personalized and biologically informed approaches in response to address the complexities of clinical practice. Integrating innovative methods with symptom-based diagnostic systems may contribute to more accurate and effective diagnostic classifications of major depression.
Source: PubMed (PMID: 42036558)View Original on PubMed
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